What does the term "model validation" refer to in the context of simulation?

The term "model validation" in the context of simulation specifically refers to the process of ensuring that the simulation accurately represents the real-world process it is intended to mimic. This involves assessing whether the model's outputs align with actual observations, confirming that the assumptions made during the modeling process reflect reality, and verifying that the model behaves as expected under various conditions.

Validation is crucial because it builds confidence in the simulation results, ensuring that decisions made based on the model are sound and reliable. By rigorously testing the model against real-world data, practitioners can identify any discrepancies and make necessary adjustments, thereby improving the overall accuracy and usefulness of the simulation.

In contrast, other options imply actions that do not contribute to validating a model. Increasing the complexity of a simulation model may lead to a more detailed representation but does not guarantee accuracy if the underlying assumptions are flawed. Creating new models without testing them fails to provide any insight into their validity. Finally, relying on subjective measures to validate outcomes could introduce bias and undermine the objective assessment required for effective model validation.